Switch AI Models at Runtime on Telnyx Edge Compute

Most AI examples hardcode the model name.

That is fine until you actually want to compare models.

If every model change requires a code edit and redeploy, experimenting gets annoying fast. The multi-model-inference-switcher example turns model choice into runtime configuration instead.

Code: https://github.com/team-telnyx/telnyx-code-examples/tree/main/multi-model-inference-switcher

What it builds

This is a TypeScript app running on Telnyx Edge Compute with the Agent SDK.

It gives you:

• an admin UI
• a model dropdown
• a chat panel
• durable conversation history
• usage stats by model
• a KV-backed active-model flag

The active model is read from Telnyx KV Storage every time /chat is called. When you switch the model from the UI or API, the next message uses the new model immediately.

No redeploy.

The flow

GET /
  -> admin UI

POST /model
  -> validate model
  -> write active-model to KV

POST /chat
  -> read active-model from KV
  -> SwitcherAgent.process(text, model)
  -> Telnyx AI Inference
  -> return reply + model

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The sample includes these models:

• moonshotai/Kimi-K2.6
• zai-org/GLM-5.2
• meta-llama/Llama-3.3-70B-Instruct

Why this is useful

Model choice is product behavior.

Changing the model can affect:

• latency
• cost
• output quality
• reasoning depth
• tone
• reliability

So it helps to make the active model observable and switchable without mixing that decision into application deploys.

API examples

Switch the active model:

curl -X POST https://multi-model-inference-switcher-<id>.telnyxcompute.com/model \
  -H "Content-Type: application/json" \
  -d '{"model":"zai-org/GLM-5.2"}'

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Send a chat message:

curl -X POST https://multi-model-inference-switcher-<id>.telnyxcompute.com/chat \
  -H "Content-Type: application/json" \
  -d '{"text":"Explain feature flags for AI models."}'

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Example response:

{
  "reply": "Feature flags let you change behavior at runtime...",
  "model": "zai-org/GLM-5.2"
}

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Inspect history and usage:

curl https://multi-model-inference-switcher-<id>.telnyxcompute.com/history

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Agent SDK primitives used

The SwitcherAgent uses:

• durable message history for chat context
• actor state for total requests and model usage
• the Telnyx binding for zero-credential AI Inference
• KV Storage for the global model flag

The inference call looks like:

this.env.TELNYX.ai.openai.chat.createCompletion({
  model,
  messages,
  max_tokens: 2000,
  temperature: 0.7,
});

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The key part is that model comes from KV, not a hardcoded constant.

Run it

git clone https://github.com/team-telnyx/telnyx-code-examples.git
cd telnyx-code-examples/multi-model-inference-switcher
npm install

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Create and seed KV:

telnyx-edge storage kv create --name "switcher-flag"
telnyx-edge storage kv key put <kv-id> active-model moonshotai/Kimi-K2.6

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Set your namespace ID in telnyx.toml, add your secret, and deploy:

telnyx-edge secrets add TELNYX_API_KEY <YOUR_API_KEY>
telnyx-edge ship

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Production notes

Before exposing this publicly, add:

• auth on the admin UI and /model
• audit logging for model changes
• a reviewed model allowlist
• fallback behavior if the active model fails
• latency and cost tracking per model
• scoped flags by environment, tenant, or cohort

The small idea here is powerful: keep your app deployed, but make model selection something you can operate.

Resources:

• Code: https://github.com/team-telnyx/telnyx-code-examples/tree/main/multi-model-inference-switcher
• Agent SDK docs: https://developers.telnyx.com/docs/agent-sdk
• Edge Compute docs: https://developers.telnyx.com/docs/edge-compute
• Telnyx AI Inference docs: https://developers.telnyx.com/docs/inference
• Telnyx AI skills and toolkits: https://github.com/team-telnyx/ai